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GLM 5.3 vs Grok 4.7

GLM 5.3 leads Grok 4.7 on coding, 70.6 to 66.7 on the Vibe Coding Index, and GLM 5.3 costs 1.4x less per output token ($4.40 vs $6.00 per million). Updated October 22, 2026; 83 benchmarks have a result for both.

Replace GLM 5.3 Replace Grok 4.7
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
54
Grok 4.7xAI
Coding
66.7
Overall
66.6
Output $/M
$6.00
29
vs boards won of 83 shared

GLM 5.3 or Grok 4.7: which to pick

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 66.7 for Grok 4.7.

Pick Grok 4.7 for work measured by LiveBench coding or LMArena WebDev, where it leads.

Model AGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved taskModel BGrok 4.7xAI66.7/1005 independent benchmarksNo cost per solved task
Shared results
83
GLM 5.3 only
17
Grok 4.7 only
16
Like-for-like categories
8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GLM 5.3 70.6 from 6 benchmarks, Grok 4.7 66.7 from 5 benchmarks.
Widest category
Math, 13.6 points apart on average over 6 shared benchmarks.
Widest single gap
LiveBench browser: GLM 5.3 0.0% against Grok 4.7 60.0%, 60.0 points apart on a 0 to 100 scale.
Output price
GLM 5.3 $4.40 against Grok 4.7 $6.00 per million output tokens, 1.4x apart.
Context window
GLM 5.3 takes 1.05M tokens, Grok 4.7 500K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGLM 5.3BarsGrok 4.7Ties
Coding88
Intelligence11
Agentic work57
Reasoning54
Math51
Knowledge52
Web design52
General204
All shared boards5429
CodingLike-for-likeSix of these boards feed the Vibe Coding Index88
Coding benchmarks for GLM 5.3 and Grok 4.7
BenchmarkGLM 5.3Grok 4.7Leader
LiveBench coding82.8%84.5%Grok 4.7
LMArena WebDev16211637Grok 4.7
AA Coding Index74.8Not measuredOnly GLM 5.3 measured
LiveCodeBench (Vals AI)80.5%Not measuredOnly GLM 5.3 measured
SWE-bench Verified (Vals AI)95.4%Not measuredOnly GLM 5.3 measured
Terminal-Bench 4 (Vals AI)38.9%28.8%GLM 5.3
Vibe Code Bench (Vals AI)78.1%86.2%Grok 4.7
Code migration (Vals AI)44.2%44.8%Grok 4.7
ProgramBench (Vals AI)1.5%0.5%GLM 5.3
IOI (Vals AI)68.4%57.7%GLM 5.3
Terminal-Bench 2.1 (Vals AI)71.5%73.4%Grok 4.7
SWE-bench Multimodal (Vals AI)Not measured42.9%Only Grok 4.7 measured
Vibe Code Bench 1-100 (Vals AI)20.0%Not measuredOnly GLM 5.3 measured
SkillsBench (Vals AI)47.5%Not measuredOnly GLM 5.3 measured
CyberBench (Vals AI)72.1%69.5%GLM 5.3
SciCode59.0%57.8%GLM 5.3
FrontierCode40.1%47.6%Grok 4.7
FrontierSWE30.2%29.5%GLM 5.3
WeirdML75.4%Not measuredOnly GLM 5.3 measured
ALE-Bench1317.4Not measuredOnly GLM 5.3 measured
LMArena Text: coding14971432GLM 5.3
LMArena Text: software and IT14931421GLM 5.3
LMArena WebDev: React16411663Grok 4.7
LMArena WebDev: HTML15951627Grok 4.7
LMArena WebDev: from an imageNot measured1586Only Grok 4.7 measured
Neither model has a result on the other 14 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
Intelligence benchmarks for GLM 5.3 and Grok 4.7
BenchmarkGLM 5.3Grok 4.7Leader
AA Intelligence Index44.846.4Grok 4.7
Epoch Capabilities Index156.0154.4GLM 5.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index57
Agentic work benchmarks for GLM 5.3 and Grok 4.7
BenchmarkGLM 5.3Grok 4.7Leader
AA Agentic Index53.1Not measuredOnly GLM 5.3 measured
Design Arena: agents building mobile apps1194Not measuredOnly GLM 5.3 measured
Design Arena: HTML slides1189Not measuredOnly GLM 5.3 measured
Design Arena: PowerPoint slides1230Not measuredOnly GLM 5.3 measured
LiveBench research73.2%86.4%Grok 4.7
LiveBench browser0.0%60.0%Grok 4.7
LiveBench long agentic35.9%50.1%Grok 4.7
SREBench (Vals AI)9.2%3.8%GLM 5.3
Finance Agent (Vals AI)55.8%52.3%GLM 5.3
Tax Agent Bench (Vals AI)39.7%34.2%GLM 5.3
Legal Research Bench (Vals AI)49.0%47.1%GLM 5.3
Harvey's Legal Agent Benchmark (Vals AI)14.6%18.3%Grok 4.7
Excel Modeling Benchmark (Vals AI)56.3%67.0%Grok 4.7
BioMysteryBench (Vals AI)Not measured69.3%Only Grok 4.7 measured
Terminal-Bench-Science (Vals AI)5.7%10.0%Grok 4.7
Vals Science IndexNot measured34.8%Only Grok 4.7 measured
Robo-bench (Vals AI)Not measured0.2%Only Grok 4.7 measured
APEX-Agents56.6%54.6%GLM 5.3
Vending-Bench 2$8,164$10,537Grok 4.7
Neither model has a result on the other 22 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index54
Reasoning benchmarks for GLM 5.3 and Grok 4.7
BenchmarkGLM 5.3Grok 4.7Leader
GPQA Diamond88.1%Not measuredOnly GLM 5.3 measured
LiveBench reasoning85.8%82.7%GLM 5.3
MysteryMechanism (Vals AI)23.0%25.2%Grok 4.7
GPQA Diamond (Artificial Analysis run)91.7%Not measuredOnly GLM 5.3 measured
Humanity's Last Exam (Artificial Analysis run)42.3%43.1%Grok 4.7
GPQA Diamond (Epoch AI run)90.9%92.7%Grok 4.7
ARC-AGINot measured90.2%Only Grok 4.7 measured
ARC-AGI-2Not measured61.4%Only Grok 4.7 measured
Chess puzzles37.0%41.0%Grok 4.7
Mystery game puzzles33.0%29.0%GLM 5.3
LMArena Text: hard prompts14901415GLM 5.3
LMArena Text: expert15141439GLM 5.3
LMArena Text: hard prompts in English14961427GLM 5.3
LMArena Vision: diagramsNot measured1254Only Grok 4.7 measured
LMArena Vision: homeworkNot measured1309Only Grok 4.7 measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index51
Math benchmarks for GLM 5.3 and Grok 4.7
BenchmarkGLM 5.3Grok 4.7Leader
OTIS Mock AIME91.1%98.1%Grok 4.7
FrontierMath, tiers 1 to 368.8%53.0%GLM 5.3
FrontierMath, tier 429.3%17.1%GLM 5.3
ProofBench49.0%26.0%GLM 5.3
LMArena Text: math14951414GLM 5.3
LMArena Text: mathematical work14971435GLM 5.3
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index52
Knowledge benchmarks for GLM 5.3 and Grok 4.7
BenchmarkGLM 5.3Grok 4.7Leader
MMLU Pro86.8%Not measuredOnly GLM 5.3 measured
LegalBench (Vals AI)84.8%84.4%GLM 5.3
TaxEval (Vals AI)72.4%Not measuredOnly GLM 5.3 measured
MedCode (Vals AI)42.9%49.5%Grok 4.7
SimpleQA Verified41.0%56.0%Grok 4.7
LMArena Text: science14981419GLM 5.3
LMArena Text: medicine and healthcare14811395GLM 5.3
LMArena Text: legal and government14731423GLM 5.3
LMArena Text: business and finance14591389GLM 5.3
Neither model has a result on the other 9 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index52
Web design benchmarks for GLM 5.3 and Grok 4.7
BenchmarkGLM 5.3Grok 4.7Leader
Design Arena: websites12951255GLM 5.3
Design Arena: UI components13141275GLM 5.3
Design Arena: game dev13111314Grok 4.7
Design Arena: data viz12601238GLM 5.3
Design Arena: 3D13321274GLM 5.3
Design Arena: all code categories13041273GLM 5.3
Design Arena: SVG1294Not measuredOnly GLM 5.3 measured
Design Arena: ASCII art1208Not measuredOnly GLM 5.3 measured
LiveBench design77.3%78.0%Grok 4.7
GeneralLike-for-likePublic boards, shown as published, kept out of the index204
General benchmarks for GLM 5.3 and Grok 4.7
BenchmarkGLM 5.3Grok 4.7Leader
Vals Index59.7%58.6%GLM 5.3
LiveBench data analysis70.2%76.9%Grok 4.7
Public Benefits Bench (Vals AI)68.5%65.6%GLM 5.3
SAGE (Vals AI)Not measured40.8%Only Grok 4.7 measured
MedScribe (Vals AI)88.8%89.4%Grok 4.7
Long context reasoning (AA-LCR)79.7%80.0%Grok 4.7
DTBench87.7%96.0%Grok 4.7
LMCA55.5%49.4%GLM 5.3
Furniture assemblyNot measured20.8%Only Grok 4.7 measured
LMArena Text14741399GLM 5.3
LMArena Text: instruction following14791406GLM 5.3
LMArena Text: creative writing14581381GLM 5.3
LMArena VisionNot measured1231Only Grok 4.7 measured
LMArena Text: English14821411GLM 5.3
LMArena Text: non-English14591386GLM 5.3
LMArena Text: Chinese15321449GLM 5.3
LMArena Text: German14881366GLM 5.3
LMArena Text: Japanese14601359GLM 5.3
LMArena Text: Korean14601383GLM 5.3
LMArena Text: French15031431GLM 5.3
LMArena Text: Spanish14621406GLM 5.3
LMArena Text: Russian14691399GLM 5.3
LMArena Text: Polish14661420GLM 5.3
LMArena Text: multi-turn14721394GLM 5.3
LMArena Text: longer queries14831416GLM 5.3
LMArena Text: writing and language14691394GLM 5.3
LMArena Text: entertainment and media14471376GLM 5.3
LMArena Vision: EnglishNot measured1229Only Grok 4.7 measured
LMArena Vision: ChineseNot measured1250Only Grok 4.7 measured
LMArena Vision: reading textNot measured1241Only Grok 4.7 measured
LMArena Vision: creative writingNot measured1217Only Grok 4.7 measured
Neither model has a result on the other 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.3 and Grok 4.7
BenchmarkGLM 5.3Grok 4.7Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GLM 5.3$0.0036Fits in one request
Grok 4.7$0.0050Fits in one request

GLM 5.3 costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.083Fits in one request
Grok 4.7$0.118Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.324Fits in one request
Grok 4.7$0.460Fits in one request

GLM 5.3 costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GLM 5.3 and Grok 4.7
SpecificationZ.ai GLM 5.3SpaceXAI Grok 4.7
MakerZ.aixAI
WeightsOpen weightsNo public weights listed
ReleasedAugust 18, 2026September 21, 2026
Context window1.05M500K
Input $/Mtok$1.40$2.00
Output $/Mtok$4.40$6.00
Coding index70.666.7
Independent measurements behind it65
Cost per solved task$0.36Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 better than Grok 4.7 for coding?

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 66.7 for Grok 4.7. Pick Grok 4.7 for work measured by LiveBench coding or LMArena WebDev, where it leads.

Which is cheaper, GLM 5.3 or Grok 4.7?

GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; Grok 4.7 costs $2.00 and $6.00. Cost per solved task is known for GLM 5.3 only.

Which has the bigger context window, GLM 5.3 or Grok 4.7?

GLM 5.3, with 1.05M tokens against 500K.

How many benchmarks is the GLM 5.3 vs Grok 4.7 comparison based on?

83 benchmarks have a result for both models as of October 22, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and Grok 4.7's on 5. Where only one model has a result, the page says so instead of naming a winner.

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